摘要
为探索通过气味分析无损快速判断速冻青稞鱼面贮藏品质的方法,利用电子鼻对不同贮藏温度与贮藏时间下的挥发性气味进行分析,将所获数据与理化品质指标值挥发性盐基氮(TVB-N)值、pH值、过氧化值(P0V)、硫代巴比妥酸(TBA)值相联系,分别建立基于电子鼻预测理化指标向传播-神经网络(BP-NN)预测模型。结果表明:基于电子鼻响应信号建立的4个预测TVB-N、pH、POV、TBA含量的BP-NN模型结果均为18x14x1,各拟合值均大于0.9;通过对随机抽取训练集和验证集进行可靠性分析发现,各模型最大的误差范围在15%以内,平均误差范围在5%以内。说明基于电子鼻的速冻青稞鱼面货架期食品安全指标预测模型预测效果良好,具备一定的可靠性。
In order to explore the m ethod of non - destructive rapid evaluation of the storage quality of quick - frozen noodles of fish and highland barley by odor analysis, the electronic nose was used to analyze the volatile odor under different storage tem perature and duration. The obtained data was correlated to build up prediction models of physical and chem ical quality indices of volatile base nitrogen ( TYB - N) v alu es, pH v a lu e, perox ide value ( P O V) , and thiobarbituric acid (T B A) values based on propagation - neural network (B P - N N). The four BP - NN models for predicting TVB N , p H , POV and TBA signal were all 18 x 14 x 1 , and all with R2 bigger than 0. 9, the maximum error range within 15% , and the average error range within 5 % .
作者
肖猛
唐婷婷
丁捷
张雨薇
王艺华
张力丹
毛永杰
XIAO Meng;TANG Tingtinng;DING Jie;ZHANG Yuwei;WANG Yihua;ZHANG Lidan;MAO Yongjie(College of Food Science,Sichuan Tourism University,Chengdu,Sichuan 610100,China;College of Food Science,Shenyang Agricultural University,Shenyang,Liaoning 110161,China)
出处
《美食研究》
北大核心
2019年第4期65-72,共8页
Journal of Researches on Dietetic Science and Culture
基金
四川省科技厅科技支撑计划(2016FZ0027)
四川旅游学院2018年科研创新团队(18SCTUTD04)
关键词
速冻青稞鱼面
电子鼻
反向传播-神经网络模型
食品检测
quick - frozen fish noodles
electronic nose
back propagation - neural network model
food de tection
